Open AccessDiagnosis of Medical Images Using Cloud-Deep Learning System
Author(s) -
Michael A. Jacobs,
Arfan Ali,
Alaa Sheta
Publication year - 2021
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v10i2.31643
Subject(s) - convolutional neural network , computer science , artificial intelligence , deep learning , task (project management) , magnetic resonance imaging , set (abstract data type) , data set , medical imaging , cloud computing , pattern recognition (psychology) , machine learning , radiology , medicine , management , economics , programming language , operating system
Diagnosis of brain tumors is one of the most severe medical problems that affect thousands of people each year in the United States. Manual classification of cancerous tumors through examination of MRI images is a difficult task even for trained professionals. It is an error-prone procedure that is dependent on the experience of the radiologist. Brain tumors, in particular, have a high level of complexity. Therefore, computer-aided diagnosis systems designed to assist with this task are of specific interest for physicians. Accurate detection and classification of brain tumors via magnetic resonance imaging (MRI) examination is a famous approach to analyze MRI images. This paper proposes a method to classify different brain tumors using a Convolutional Neural Network (CNN). We explore the performance of several CNN architectures and examine if decreasing the input image resolution affects the model's accuracy. The dataset used to train the model has initially been 3064 MRI scans. We augmented the data set to 8544 MRI scans to balance the available classes of images. The results show that the design of a suitable CNN architecture can significantly better diagnose medical images. The developed model classification performance was up to 97\% accuracy.